def train(self, dataset, *args, **kwargs):
"""
Method trains the intent_model using batches and validation
Args:
dataset: instance of class Dataset
Returns: None
"""
updates = 0
val_loss = 1e100
val_increase = 0
epochs_done = 0
n_train_samples = len(dataset.data['train'])
print('\n____Training over {} samples____\n\n'.format(n_train_samples))
while epochs_done < self.opt['epochs']:
batch_gen = dataset.batch_generator(batch_size=self.opt['batch_size'],
data_type='train')
for step, batch in enumerate(batch_gen):
metrics_values = self.train_on_batch(batch)
updates += 1
if self.opt['verbose'] and step % 500 == 0:
log_metrics(names=self.metrics_names,
values=metrics_values,
updates=updates,
mode='train')
epochs_done += 1
if epochs_done % self.opt['val_every_n_epochs'] == 0:
if 'valid' in dataset.data.keys():
valid_batch_gen = dataset.batch_generator(batch_size=self.opt['batch_size'],
data_type='valid')
valid_metrics_values = []
for valid_step, valid_batch in enumerate(valid_batch_gen):
valid_metrics_values.append(self.infer_on_batch(valid_batch[0],
labels=valid_batch[1]))
valid_metrics_values = np.mean(np.asarray(valid_metrics_values), axis=0)
log_metrics(names=self.metrics_names,
values=valid_metrics_values,
mode='valid')
if valid_metrics_values[0] > val_loss:
val_increase += 1
print("__Validation impatience {} out of {}".format(
val_increase, self.opt['val_patience']))
if val_increase == self.opt['val_patience']:
print("___Stop training: validation is out of patience___")
break
else:
val_increase = 0
val_loss = valid_metrics_values[0]
print('epochs_done: {}'.format(epochs_done))
self.save()
def train(self, dataset, *args, **kwargs):
"""
Method trains the intent_model using batches and validation
Args:
dataset: instance of class Dataset
Returns: None
"""
updates = 0
val_loss = 1e100
val_increase = 0
epochs_done = 0
n_train_samples = len(dataset.data['train'])
print('\n____Training over {} samples____\n\n'.format(n_train_samples))
while epochs_done < self.opt['epochs']:
batch_gen = dataset.batch_generator(
batch_size=self.opt['batch_size'], data_type='train')
for step, batch in enumerate(batch_gen):
metrics_values = self.train_on_batch(batch)
updates += 1
if self.opt['verbose'] and step % 500 == 0:
log_metrics(names=self.metrics_names,
values=metrics_values,
updates=updates,
mode='train')
epochs_done += 1
if epochs_done % self.opt['val_every_n_epochs'] == 0:
if 'valid' in dataset.data.keys():
valid_batch_gen = dataset.batch_generator(
batch_size=self.opt['batch_size'], data_type='valid')
valid_metrics_values = []
for valid_step, valid_batch in enumerate(valid_batch_gen):
valid_metrics_values.append(
self.infer_on_batch(valid_batch[0],
labels=valid_batch[1]))
valid_metrics_values = np.mean(
np.asarray(valid_metrics_values), axis=0)
log_metrics(names=self.metrics_names,
values=valid_metrics_values,
mode='valid')
if valid_metrics_values[0] > val_loss:
val_increase += 1
print("__Validation impatience {} out of {}".format(
val_increase, self.opt['val_patience']))
if val_increase == self.opt['val_patience']:
print(
"___Stop training: validation is out of patience___"
)
break
else:
val_increase = 0
val_loss = valid_metrics_values[0]
print('epochs_done: {}'.format(epochs_done))
self.save()
def train(self, dataset, *args, **kwargs):
"""
Method trains the intent_model using batches and validation
Args:
dataset: instance of class Dataset
Returns: None
"""
updates = 0
val_loss = 1e100
val_increase = 0
epochs_done = 0
n_train_samples = len(dataset.data['train'])
valid_iter_all = dataset.iter_all(data_type='valid')
valid_x = []
valid_y = []
for valid_i, valid_sample in enumerate(valid_iter_all):
valid_x.append(valid_sample[0])
valid_y.append(valid_sample[1])
valid_x = self.texts2vec(valid_x)
valid_y = labels2onehot(valid_y, classes=self.classes)
# print('\n____Training over {} samples____\n\n'.format(n_train_samples))
while epochs_done < self.opt['epochs']:
batch_gen = dataset.batch_generator(
batch_size=self.opt['batch_size'], data_type='train')
for step, batch in enumerate(batch_gen):
metrics_values = self.train_on_batch(batch)
updates += 1
if self.opt['verbose'] and step % 50 == 0:
log_metrics(names=self.metrics_names,
values=metrics_values,
updates=updates,
mode='train')
epochs_done += 1
if epochs_done % self.opt['val_every_n_epochs'] == 0:
if 'valid' in dataset.data.keys():
valid_metrics_values = self.model.test_on_batch(x=valid_x,
y=valid_y)
log_metrics(names=self.metrics_names,
values=valid_metrics_values,
mode='valid')
if valid_metrics_values[0] > val_loss:
val_increase += 1
# print("__Validation impatience {} out of {}".format(
# val_increase, self.opt['val_patience']))
if val_increase == self.opt['val_patience']:
# print("___Stop training: validation is out of patience___")
break
else:
val_increase = 0
val_loss = valid_metrics_values[0]
# print('epochs_done: {}'.format(epochs_done))
self.save()